Gastos Casa
17 March, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 30/12/2024 | Otros | 47484 | Andrés | viaje a brasil (duplicar para q cargue sobre tami) |
| 4/1/2025 | Diosi | 53999 | Andrés | n y d pumpkin 7.5 |
| 4/1/2025 | Comida | 15260 | Andrés | NA |
| 6/1/2025 | Comida | 40988 | Tami | Supermercado |
| 8/1/2025 | Pago cámaras MB | 20000 | Tami | NA |
| 13/1/2025 | Comida | 67387 | Tami | Supermercado |
| 20/1/2025 | Comida | 21692 | Andrés | gnoccis |
| 20/1/2025 | Comida | 86884 | Tami | Supermercado |
| 21/1/2025 | Comida | 21525 | Andrés | piwen |
| 23/1/2025 | VTR | 21990 | Andrés | NA |
| 25/1/2025 | Diosi | 20000 | Andrés | arena diosi |
| 27/1/2025 | Comida | 71516 | Tami | Supermercado |
| 30/1/2025 | Electricidad | 55000 | Andrés | NA |
| 6/2/2025 | Comida | 52730 | Andrés | supermercado (no cobre el otro de 25k pq muchas son cosas mías) |
| 9/2/2025 | Comida | 12500 | Andrés | NA |
| 17/2/2025 | Comida | 7940 | Andrés | NA |
| 18/2/2025 | Electricidad | 64888 | Andrés | la puse por adelantado para que no se me olvide |
| 18/2/2025 | Comida | 17820 | Tami | Supermercado |
| 23/2/2025 | Comida | 86908 | Tami | Supermercado |
| 27/2/2025 | Comida | 10000 | Andrés | NA |
| 26/2/2025 | Comida | 4620 | Andrés | NA |
| 1/3/2025 | Comida | 2300 | Tami | Supermercado |
| 2/3/2025 | Comida | 102058 | Tami | Supermercado |
| 3/3/2025 | Comida | 9370 | Andrés | NA |
| 9/3/2025 | Comida | 61916 | Tami | Supermercado |
| 11/3/2025 | Comida | 27021 | Andrés | NA |
| 11/3/2025 | Enceres | 13190 | Tami | 40 rollos confort |
| 15/3/2025 | Comida | 78061 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.8127e+08 2 4.9577 0.0072 **
## lag_depvar 2.6293e+11 1 2656.8350 <2e-16 ***
## Residuals 8.1052e+10 819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1909.685 16367.36 0.1519344
## 2-0 31339.384 23110.914 39567.85 0.0000000
## 2-1 24110.546 19341.962 28879.13 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 768 200415.71 2 171565.29
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## 771 195266.57 2 197558.86
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## 773 85493.71 2 203144.29
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## 776 40161.71 2 36232.14
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## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
## 819 29568.00 2 36772.29
## 820 42362.00 2 29568.00
## 821 42566.29 2 42362.00
## 822 39596.00 2 42566.29
## 823 32925.00 2 39596.00
## 824 43416.57 2 32925.00
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 667 53573.65 22345.415
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2022.105726 4041.751572 -539.406381 2436.914665 -2972.270673
## 7 8 9 10 11
## 517.786869 -5657.258620 -1186.105314 -3964.254613 -414.315656
## 12 13 14 15 16
## -4936.572346 -1604.076146 -894.436538 382.378048 -3239.063316
## 17 18 19 20 21
## -372.950706 -2125.812057 6608.861873 -1529.340683 -1207.850458
## 22 23 24 25 26
## 1476.390936 -1187.106266 234.591067 1694.522972 -7103.734325
## 27 28 29 30 31
## 950.038185 8193.847730 414.570580 -17.571408 -2403.868233
## 32 33 34 35 36
## 1574.556178 4569.981751 1121.902335 2386.266271 -1874.053881
## 37 38 39 40 41
## 4603.580863 4301.193796 -2280.145647 -2983.977860 -1110.626381
## 42 43 44 45 46
## -10741.232478 7295.790156 2558.578710 1366.481821 8104.030073
## 47 48 49 50 51
## 680.422477 6523.351786 6705.863555 -5893.698499 -4801.921019
## 52 53 54 55 56
## -5062.581666 -7928.096943 6134.856710 -4075.812944 -4891.268272
## 57 58 59 60 61
## 3861.311098 890.340380 -30.485426 143.631601 -4995.321380
## 62 63 64 65 66
## 18130.647977 3633.407421 -3654.565428 5920.150238 7336.087433
## 67 68 69 70 71
## 14627.742166 1675.046918 -13229.427644 -1312.808812 4638.572922
## 72 73 74 75 76
## -4907.244338 -4407.610942 -10497.389386 2473.444019 -5395.274620
## 77 78 79 80 81
## 1071.140213 -6860.321878 556.457157 -2346.442609 -2685.183385
## 82 83 84 85 86
## -3922.496729 -526.757721 2324.463748 3769.895944 480.841880
## 87 88 89 90 91
## -481.458612 199.557224 4304.341404 -1163.630488 1151.002488
## 92 93 94 95 96
## -2065.077700 -1043.555828 178.865022 275.777183 -7483.357696
## 97 98 99 100 101
## 2397.120723 -8599.251931 -2932.102714 -4029.935648 -1725.535964
## 102 103 104 105 106
## -1250.167195 3191.762610 -2334.542328 2602.161866 -1152.913892
## 107 108 109 110 111
## 976.042129 2591.373493 -3152.301181 -4719.174356 -843.770278
## 112 113 114 115 116
## 1909.815194 11697.869269 -1246.326424 2666.080498 4259.032103
## 117 118 119 120 121
## 3496.684554 -1107.342268 -4722.005403 -3725.934355 2320.654951
## 122 123 124 125 126
## -1733.273484 1341.081565 8858.045555 841.331927 124.944354
## 127 128 129 130 131
## -2526.079674 2652.555220 7048.703256 1004.663952 -8506.780470
## 132 133 134 135 136
## 1748.244113 4133.600204 -3168.233021 -1421.403340 -854.480193
## 137 138 139 140 141
## -3879.846477 1185.736800 -493.830025 -2911.797626 1721.662893
## 142 143 144 145 146
## -1879.088315 -7826.318054 2047.140328 -3474.293935 2109.202093
## 147 148 149 150 151
## -252.766060 1027.272994 -356.289327 1355.022630 1188.197200
## 152 153 154 155 156
## 3357.147525 -4863.482137 -1172.820608 -3233.609691 5960.731971
## 157 158 159 160 161
## 9746.293213 -3628.782064 -4973.770104 3410.790090 -2.095965
## 162 163 164 165 166
## 2498.208488 -6111.409830 -6944.786706 3963.164268 17194.053019
## 167 168 169 170 171
## 3407.807487 -621.967486 -2668.188030 -1323.130700 3372.942906
## 172 173 174 175 176
## -449.485164 -8296.032947 2651.719251 4109.671481 404.724751
## 177 178 179 180 181
## 8529.069142 -9479.193322 -3691.255338 -10960.513296 -11445.113060
## 182 183 184 185 186
## 1040.018649 9093.958928 -1642.344703 5716.470853 6334.176453
## 187 188 189 190 191
## 12927.018071 8180.663450 -4325.341718 2207.711132 10107.655294
## 192 193 194 195 196
## -1919.019307 -2716.280459 -10547.478371 -6614.410297 991.855397
## 197 198 199 200 201
## -5476.723030 -10031.589618 5162.012919 -3298.303439 -1938.356726
## 202 203 204 205 206
## -1028.390007 6270.074215 9644.974303 322.864508 2668.058064
## 207 208 209 210 211
## 2836.956024 5519.363766 12560.965695 -5978.027799 -11573.211962
## 212 213 214 215 216
## -5921.884571 -10832.019562 -5301.928806 1307.676585 -13233.240657
## 217 218 219 220 221
## 16186.552158 7576.357552 1280.681157 26438.021269 12229.555197
## 222 223 224 225 226
## 7020.203627 13704.974672 -4252.672912 -2064.682407 3464.313174
## 227 228 229 230 231
## 47.940768 2441.458037 8703.372435 5523.342349 -2212.149165
## 232 233 234 235 236
## -2121.911999 9141.782763 -11802.076438 -7552.548385 -8795.386925
## 237 238 239 240 241
## -10340.422800 2854.951185 1123.146656 -8528.880294 -9208.178675
## 242 243 244 245 246
## 8889.132452 -7989.307630 2274.827748 -10520.514344 -4258.101469
## 247 248 249 250 251
## 1221.613117 795.253816 -12529.168717 3447.941171 1855.530285
## 252 253 254 255 256
## 3996.888707 1908.649064 -1393.233314 10906.409013 20624.623508
## 257 258 259 260 261
## 2898.261153 -4574.034767 3818.256580 -1991.196058 3447.209614
## 262 263 264 265 266
## -5146.087099 -11175.119261 -4986.620914 -770.302261 -5436.633677
## 267 268 269 270 271
## 8538.806765 -4540.392747 3937.099675 -2369.769233 4171.535738
## 272 273 274 275 276
## 438.973613 7030.947149 -1700.627662 11740.353409 -4896.687689
## 277 278 279 280 281
## 1425.011175 -675.087703 7551.489786 -5373.973514 -3031.630081
## 282 283 284 285 286
## -11551.774591 -2928.939348 18402.061245 7483.048643 2416.146765
## 287 288 289 290 291
## -949.861293 590.410887 6083.956822 6555.138286 -19112.719959
## 292 293 294 295 296
## -11420.706200 -8368.575727 9441.517138 2820.816047 -1438.537731
## 297 298 299 300 301
## 27146.542927 9731.877624 4544.810696 9156.484325 2477.268071
## 302 303 304 305 306
## -1408.499935 7534.793189 -24669.507108 -3823.183818 -447.127569
## 307 308 309 310 311
## -7235.152181 -4213.520899 2704.690633 -9427.987323 -3435.570107
## 312 313 314 315 316
## -8381.952369 1394.257629 -3331.233924 1874.246635 -4268.358003
## 317 318 319 320 321
## 27267.666684 -1015.882526 3003.694116 10534.188572 5260.870553
## 322 323 324 325 326
## 32040.849610 4676.374971 -21367.654408 1450.366888 773.300547
## 327 328 329 330 331
## -6795.509596 -2033.401980 -33553.870797 749.347017 -2438.795347
## 332 333 334 335 336
## -223.052179 -3299.901595 3961.644446 -579.910635 -7096.888871
## 337 338 339 340 341
## -3238.940033 -2307.563490 -7792.929547 3760.136013 -1485.504010
## 342 343 344 345 346
## -1853.725930 -1109.865514 57.868764 355.841238 -1752.288405
## 347 348 349 350 351
## -9580.267408 -13315.172669 2244.878236 -4409.087658 -3738.221075
## 352 353 354 355 356
## -6056.427620 1685.702842 1299.760936 2651.357363 -3889.953277
## 357 358 359 360 361
## -634.388095 552.036574 6877.107740 104.585839 -215.707663
## 362 363 364 365 366
## 2402.134582 -2944.409646 -1061.113594 -8924.519609 -4771.186338
## 367 368 369 370 371
## -6341.518597 -5057.426511 -7346.693111 4942.412641 265.036928
## 372 373 374 375 376
## 7002.738036 -7791.883470 -2390.190706 -3511.220058 -2581.880305
## 377 378 379 380 381
## -12568.462480 1836.957194 -10720.018087 5644.590940 9245.663392
## 382 383 384 385 386
## 2983.541331 -2562.142760 1446.261664 6573.096854 11205.115079
## 387 388 389 390 391
## -6061.544792 -5598.423776 -371.505228 8347.929771 1559.880114
## 392 393 394 395 396
## 10959.533636 -10189.519488 2509.782122 437.704915 287.193652
## 397 398 399 400 401
## -928.276706 -831.326085 -14749.897612 8333.771867 -1405.066057
## 402 403 404 405 406
## -1587.878884 6774.965164 -8170.115922 -1493.419327 -2719.427845
## 407 408 409 410 411
## -5992.936039 -3004.250366 -4050.170883 -8872.881550 6051.692464
## 412 413 414 415 416
## 1520.687961 -7507.705197 -7797.317900 14144.279743 3658.519790
## 417 418 419 420 421
## 4307.852275 -8246.672347 -4916.950883 -2752.123137 2679.001017
## 422 423 424 425 426
## -14168.794692 -2885.768112 -9187.118510 2959.256375 6896.839012
## 427 428 429 430 431
## 6450.498131 -4151.735735 -4269.502444 -4855.112452 -1905.063521
## 432 433 434 435 436
## -5825.277327 -6720.101552 -6020.855442 -1447.699819 -908.306880
## 437 438 439 440 441
## -5043.919102 2524.279047 4756.991788 -5172.927743 -2258.434909
## 442 443 444 445 446
## 1477.592822 -3951.999501 2731.301996 -6703.702238 -12211.736096
## 447 448 449 450 451
## -4565.408567 9599.639478 -2133.850585 4654.375912 -5998.502493
## 452 453 454 455 456
## -1229.735808 275.378920 2909.728619 -12404.269187 3283.609238
## 457 458 459 460 461
## -6810.242282 6436.353602 2888.995733 2365.229327 -4002.107830
## 462 463 464 465 466
## 1951.531228 -161.019172 1637.233709 -686.800587 3186.130623
## 467 468 469 470 471
## -2819.322879 5637.803242 -7134.741589 -3122.518421 -2349.480808
## 472 473 474 475 476
## -4798.556523 2880.676498 7664.559906 -6186.704317 1342.116513
## 477 478 479 480 481
## -6328.642004 -2967.662972 1898.201298 -13056.322518 -9830.003897
## 482 483 484 485 486
## -1244.714344 -28.885326 -1022.709895 -1408.550165 -9655.437288
## 487 488 489 490 491
## 11055.098975 6136.121326 7288.133196 -5603.693437 5223.307930
## 492 493 494 495 496
## 9124.124688 5845.629264 -13703.541536 -10731.691006 -3560.544718
## 497 498 499 500 501
## -1213.422948 -631.253578 -7734.822986 530.589344 4198.254415
## 502 503 504 505 506
## 5395.404140 520.187913 -63.665344 -7384.345188 454.408446
## 507 508 509 510 511
## -5169.419859 1729.760044 -1411.330802 -8270.810790 -685.066224
## 512 513 514 515 516
## -2761.634224 -670.193338 1245.293938 -9594.102295 -7830.788356
## 517 518 519 520 521
## 24243.555516 9670.875103 5683.410497 -5552.968007 2608.384352
## 522 523 524 525 526
## 16820.198588 11207.051304 -24453.736018 -5250.220262 -3899.136110
## 527 528 529 530 531
## 4423.016333 -525.722914 -11269.250448 4272.092130 13768.581037
## 532 533 534 535 536
## -5177.689024 4196.780717 5360.704490 -2005.407996 -4743.617505
## 537 538 539 540 541
## -7253.431365 -2247.544048 8184.263786 -46.914144 -8314.215367
## 542 543 544 545 546
## 1679.135171 -745.257439 222.543290 -11176.873595 -11162.342808
## 547 548 549 550 551
## 1980.905295 6926.249595 -1431.278756 724.824556 -7840.271584
## 552 553 554 555 556
## 8474.034617 774.200037 -12082.888315 9070.911995 8521.108867
## 557 558 559 560 561
## -76.901970 4677.391496 -3769.647087 13930.684962 21262.611612
## 562 563 564 565 566
## -6786.995436 -9959.800820 6547.567454 -23.396295 3212.592156
## 567 568 569 570 571
## -7625.708464 -17515.789656 6535.086721 6279.158875 1721.905815
## 572 573 574 575 576
## 2913.906311 1576.937569 -2360.348296 14535.774786 -9888.579432
## 577 578 579 580 581
## -6436.661546 8548.855549 2661.143188 -6750.116784 7336.715514
## 582 583 584 585 586
## -4000.593947 -2955.436698 15537.634439 -14727.362186 8266.570504
## 587 588 589 590 591
## -124.269419 -6402.291745 -913.453697 98.105413 -10806.362292
## 592 593 594 595 596
## 1689.086016 -7262.067906 2977.451046 8759.127232 -7647.330158
## 597 598 599 600 601
## 5735.456239 2592.423881 6701.896920 -3371.005955 5985.377746
## 602 603 604 605 606
## -8485.174992 2106.993024 1113.775202 2978.928513 1325.535939
## 607 608 609 610 611
## 225.185895 -5980.534070 7931.878553 -1357.730004 -2738.835904
## 612 613 614 615 616
## -3603.767066 -8362.196387 11858.517056 4786.588357 -9481.534300
## 617 618 619 620 621
## 11500.053371 5878.117876 -5763.673151 26198.196031 -13082.789575
## 622 623 624 625 626
## -6971.354376 3000.010625 -4324.254202 -10736.399010 11195.379377
## 627 628 629 630 631
## -21781.094499 -2477.556765 8615.640265 11037.586463 -1694.190737
## 632 633 634 635 636
## 33150.622116 -6841.934722 5501.124042 5172.431148 -2503.529971
## 637 638 639 640 641
## -5559.072594 -2123.799431 -12600.399283 -2360.930842 -1996.448302
## 642 643 644 645 646
## -2624.330177 -2954.186058 1727.074712 4333.237473 16848.881529
## 647 648 649 650 651
## 18374.880639 643.032926 4557.154108 10373.763265 19886.935175
## 652 653 654 655 656
## 428.715876 -28357.466953 -1514.580719 -2450.251128 1725.584268
## 657 658 659 660 661
## -3334.183835 -10746.424198 1579.921219 4135.898375 -1110.951258
## 662 663 664 665 666
## 12932.804048 1219.390036 1673.241008 -11832.003859 1280.456794
## 667 668 669 670 671
## 1085.426557 -5269.909374 -7495.563366 2004.884624 -3781.779011
## 672 673 674 675 676
## 2613.251001 -3449.893769 -9399.069855 -8344.632262 -3000.128912
## 677 678 679 680 681
## 149.226647 2814.328033 665.151922 -3881.824550 -1857.340686
## 682 683 684 685 686
## -1367.236435 -8292.880264 4615.833546 -2295.186165 -1449.558607
## 687 688 689 690 691
## 535.166988 10795.111696 9757.941470 10506.325480 -9803.085153
## 692 693 694 695 696
## -3659.251283 -3231.891490 5788.880360 -10482.349382 -7978.365695
## 697 698 699 700 701
## -8658.827930 -6304.165373 -4759.997094 3065.109885 -4434.735451
## 702 703 704 705 706
## -1926.587203 4191.661022 31059.943318 9421.734837 23343.588867
## 707 708 709 710 711
## 1565.543900 8220.815620 22822.533738 6455.299276 -18298.472048
## 712 713 714 715 716
## 4759.735585 -5503.061906 -149.379110 434.496863 -17309.608438
## 717 718 719 720 721
## -5289.762765 3313.871092 -3037.948924 -13000.282108 4269.025836
## 722 723 724 725 726
## -5572.727049 729.657121 -3949.954091 -12460.480832 1363.801615
## 727 728 729 730 731
## -1875.102404 -9786.384268 17266.470414 1753.916276 -2746.479067
## 732 733 734 735 736
## 5693.523484 -8658.032066 -742.939242 8118.520675 -15379.522398
## 737 738 739 740 741
## -5925.011520 7397.747683 -4803.876307 144.702208 1809.941286
## 742 743 744 745 746
## -1976.569897 -5187.882571 6396.470431 -6298.381035 22680.477816
## 747 748 749 750 751
## 7788.027868 -1990.917990 -7327.940091 23385.014532 -4340.650515
## 752 753 754 755 756
## 1354.562446 -14462.194707 56083.260165 26856.814481 15022.877354
## 757 758 759 760 761
## -10717.175373 10554.049834 7262.036926 5759.452309 -46436.065927
## 762 763 764 765 766
## -16180.536727 968.032226 -2517.435004 -3456.825537 122846.372812
## 767 768 769 770 771
## 19250.846951 43660.215557 22514.510373 12005.362471 15781.105443
## 772 773 774 775 776
## 25663.299158 -98875.905803 -6769.010136 -35838.784501 1747.702969
## 777 778 779 780 781
## -1220.351087 3404.140538 -7409.122862 -1436.390881 -1936.830712
## 782 783 784 785 786
## 3432.980152 -7148.880777 -2227.505480 3929.016866 2272.547183
## 787 788 789 790 791
## -2753.033738 -4040.034048 1748.016327 2861.468204 -44.698772
## 792 793 794 795 796
## -6696.512698 -5772.528071 -1135.221653 -1251.296951 -7805.643898
## 797 798 799 800 801
## -2339.602893 -3253.997456 -2651.101792 10738.757415 2249.743504
## 802 803 804 805 806
## 7086.975078 2927.581145 -5444.930720 8194.013401 9877.753905
## 807 808 809 810 811
## -10602.767855 -7390.543364 -7495.187545 3019.429837 4205.847135
## 812 813 814 815 816
## -2260.238602 -14154.417045 -4093.238916 6277.810383 8251.153930
## 817 818 819 820 821
## -9663.619522 -7735.487977 -9318.336926 9775.419875 -1207.952939
## 822 823 824
## -4356.875422 -8430.521795 7894.477025
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17247.18 20097.25 24355.55 24073.23 26428.98 23758.93 24475.97 19703.25
## 10 11 12 13 14 15 16 17
## 19439.54 16779.60 17557.86 14283.93 14335.15 15000.48 16698.78 15017.09
## 18 19 20 21 22 23 24 25
## 16052.81 15425.71 22515.34 21598.42 21077.75 22969.68 22294.98 22948.19
## 26 27 28 29 30 31 32 33
## 24796.02 18718.25 20446.15 28291.43 28349.14 28021.73 25648.73 27052.59
## 34 35 36 37 38 39 40 41
## 30899.53 31248.31 32658.91 30166.99 34141.81 37353.15 34406.26 31213.91
## 42 43 44 45 46 47 48 49
## 30060.52 20630.50 28156.85 30595.80 31686.11 38531.15 38025.22 42692.14
## 50 51 52 53 54 55 56 57
## 46932.70 39623.21 34186.15 29203.81 22341.29 28637.67 25214.84 21508.69
## 58 59 60 61 62 63 64 65
## 25921.52 27182.34 27479.65 27891.89 23758.64 40366.74 42212.57 37453.71
## 66 67 68 69 70 71 72 73
## 41664.91 46585.54 57264.52 55276.28 40504.52 38007.86 41028.82 35323.18
## 74 75 76 77 78 79 80 81
## 30770.82 21464.84 24669.56 20591.15 22679.32 17569.69 19587.16 18812.90
## 82 83 84 85 86 87 88 89
## 17839.64 15906.61 17185.68 20797.39 25219.59 26210.46 26235.44 26852.80
## 90 91 92 93 94 95 96 97
## 30982.06 29811.43 30811.79 28874.27 28073.28 28441.79 28848.79 22419.74
## 98 99 100 101 102 103 104 105
## 25437.82 18461.25 17316.22 15354.96 15655.02 16333.09 20810.26 19892.84
## 106 107 108 109 110 111 112 113
## 23407.49 23197.24 24875.06 27754.73 25250.32 21690.20 21965.90 24614.85
## 114 115 116 117 118 119 120 121
## 35490.33 33681.35 35520.68 38522.03 40479.91 38166.01 32981.79 29319.49
## 122 123 124 125 126 127 128 129
## 31404.42 29682.63 30865.38 38472.81 38114.91 37175.51 34035.87 35818.87
## 130 131 132 133 134 135 136 137
## 41222.19 40661.92 31854.76 33120.83 36313.80 32720.83 31106.48 30190.56
## 138 139 140 141 142 143 144 145
## 26744.12 28159.97 27929.37 25613.34 27639.80 26263.18 19858.86 22892.44
## 146 147 148 149 150 151 152 153
## 20716.94 23697.05 24237.58 25829.58 26011.83 27667.66 28969.71 32004.91
## 154 155 156 157 158 159 160 161
## 27470.53 26732.75 24285.55 30185.56 41649.21 39977.77 37340.07 42365.38
## 162 163 164 165 166 167 168 169
## 43775.36 47194.70 42656.07 37958.55 43389.23 59707.76 61922.11 60334.62
## 170 171 172 173 174 175 176 177
## 57157.13 55554.77 58260.06 57283.18 49567.57 52393.90 56140.28 56176.50
## 178 179 180 181 182 183 184 185
## 63312.48 53805.26 50552.94 41352.40 32883.27 36395.04 46508.63 45964.10
## 186 187 188 189 190 191 192 193
## 51922.82 57673.55 68467.34 73755.48 67443.86 67637.49 74714.88 70386.99
## 194 195 196 197 198 199 200 201
## 65905.34 55138.41 49162.57 50588.29 46178.59 38339.56 44770.73 42996.36
## 202 203 204 205 206 207 208 209
## 42633.96 43112.78 49913.60 58811.71 58440.94 60167.47 61824.92 65619.89
## 210 211 212 213 214 215 216 217
## 75095.88 67170.78 55348.03 49951.45 40938.79 37893.47 41010.24 31020.45
## 218 219 220 221 222 223 224 225
## 48010.93 55339.03 56241.84 79030.02 86532.51 88537.74 96136.67 87078.54
## 226 227 228 229 230 231 232 233
## 81070.97 80652.49 77299.11 76459.77 81201.51 82567.15 76997.05 72205.22
## 234 235 236 237 238 239 240 241
## 77864.51 64498.98 56527.53 48470.14 40073.33 44269.42 46424.31 39868.46
## 242 243 244 245 246 247 248 249
## 33541.72 43834.45 38075.60 42015.23 34271.39 32975.96 36634.89 39461.60
## 250 251 252 253 254 255 256 257
## 30281.92 36225.90 40031.11 45231.07 47952.09 47444.16 57755.38 75270.02
## 258 259 260 261 262 263 264 265
## 75084.89 68388.89 69872.20 66089.22 67536.80 61288.26 50552.19 46575.59
## 266 267 268 269 270 271 272 273
## 46785.21 42888.05 51700.96 47970.33 52121.20 50235.89 54307.31 54603.62
## 274 275 276 277 278 279 280 281
## 60627.06 58258.93 67941.54 61860.27 62070.52 60417.94 66166.54 59890.77
## 282 283 284 285 286 287 288 289
## 56451.20 45993.08 44388.22 61637.67 67173.28 67583.15 64998.16 64084.61
## 290 291 292 293 294 295 296 297
## 68089.58 72003.72 52981.28 43073.43 37078.48 47410.18 50655.25 49768.31
## 298 299 300 301 302 303 304 305
## 73988.84 79940.19 80608.52 85225.59 83422.36 78447.64 81917.94 56791.61
## 306 307 308 309 310 311 312 313
## 53048.98 52728.44 46512.38 43719.02 47325.99 39870.71 38591.52 33147.60
## 314 315 316 317 318 319 320 321
## 36935.95 36116.47 39951.79 37934.19 63746.45 61585.45 63210.67 71216.84
## 322 323 324 325 326 327 328 329
## 73606.58 99113.91 97489.94 73295.78 72092.41 70448.08 62391.69 59511.01
## 330 331 332 333 334 335 336 337
## 29429.08 33120.37 33560.34 35882.62 35222.78 40995.62 42072.32 37315.08
## 338 339 340 341 342 343 344 345
## 36528.71 36655.50 31969.72 37974.79 38638.87 38897.58 39774.27 41562.02
## 346 347 348 349 350 351 352 353
## 43385.86 43137.27 36074.74 26632.98 31983.09 30842.94 30432.57 28046.58
## 354 355 356 357 358 359 360 361
## 32730.24 36488.36 40956.52 39143.67 40405.25 42545.89 49948.70 50499.85
## 362 363 364 365 366 367 368 369
## 50701.72 53167.41 50648.26 50092.23 42729.90 39923.80 36096.86 33873.26
## 370 371 372 373 374 375 376 377
## 29927.02 37222.39 39511.69 47405.31 41370.76 40817.36 39353.17 38885.46
## 378 379 380 381 382 383 384 385
## 29743.76 34346.59 27391.12 35618.91 45962.60 49531.71 47803.31 49797.05
## 386 387 388 389 390 391 392 393
## 56023.60 65518.83 58723.14 53185.65 52914.07 60301.26 60825.18 69502.81
## 394 395 396 397 398 399 400 401
## 58597.22 60165.72 59725.38 59208.71 57694.04 56454.33 43199.23 51793.78
## 402 403 404 405 406 407 408 409
## 50793.16 49758.32 56166.26 48700.99 48011.43 46336.36 42009.11 40838.60
## 410 411 412 413 414 415 416 417
## 38900.45 32988.45 40869.45 43798.85 38465.60 33548.72 48435.91 52284.72
## 418 419 420 421 422 423 424 425
## 56218.10 48679.38 44998.84 43673.43 47263.65 35670.63 35399.55 29652.32
## 426 427 428 429 430 431 432 433
## 35248.02 43584.36 50483.74 47245.79 44311.40 41233.35 41121.42 37595.53
## 434 435 436 437 438 439 440 441
## 33729.86 30960.99 32538.74 34390.06 32392.58 37263.87 43475.93 40224.86
## 442 443 444 445 446 447 448 449
## 39930.55 42940.14 40823.98 44817.70 40059.59 31082.41 29918.65 41287.56
## 450 451 452 453 454 455 456 457
## 40968.77 46625.93 42257.45 42607.48 44229.70 47951.84 37815.39 42669.81
## 458 459 460 461 462 463 464 465
## 38088.22 45665.29 49189.06 51812.39 48538.47 50881.73 51083.48 52832.37
## 466 467 468 469 470 471 472 473
## 52329.44 55276.32 52601.77 57658.31 50911.09 48519.48 47104.13 43724.89
## 474 475 476 477 478 479 480 481
## 47485.01 54956.28 49377.31 51082.36 45865.66 44242.94 47078.89 36481.86
## 482 483 484 485 486 487 488 489
## 30036.57 31907.89 34607.42 36098.98 37065.87 30699.90 43243.45 49910.72
## 490 491 492 493 494 495 496 497
## 56748.26 51454.12 56292.30 63934.09 67749.54 53991.26 44559.12 42581.99
## 498 499 500 501 502 503 504 505
## 42905.54 43697.54 38178.41 40579.89 45887.02 51574.67 52285.09 52395.77
## 506 507 508 509 510 511 512 513
## 46091.02 47432.42 43687.67 46446.05 46111.38 39820.49 40952.78 40127.05
## 514 515 516 517 518 519 520 521
## 41233.85 43876.67 36709.22 31983.59 55898.55 64067.88 67724.68 61096.76
## 522 523 524 525 526 527 528 529
## 62437.66 76037.66 83021.74 57945.51 52810.14 49500.98 53884.58 53390.39
## 530 531 532 533 534 535 536 537
## 43563.62 48560.70 61234.55 55749.65 59150.87 63142.84 60192.33 55217.86
## 538 539 540 541 542 543 544 545
## 48673.26 47327.74 55273.20 55023.36 47575.58 49801.54 49628.03 50322.59
## 546 547 548 549 550 551 552 553
## 40961.77 32788.95 37135.32 45260.42 45057.18 46764.84 40768.39 49790.80
## 554 555 556 557 558 559 560 561
## 50947.32 40715.80 50266.75 58137.76 57502.04 61103.50 56866.32 68639.10
## 562 563 564 565 566 567 568 569
## 85345.14 75425.80 63977.43 68401.25 66523.69 67711.57 59272.79 43245.20
## 570 571 572 573 574 575 576 577
## 50261.13 56172.38 57356.38 59434.06 60081.78 57205.23 69464.58 58826.95
## 578 579 580 581 582 583 584 585
## 52543.43 60152.86 61658.40 54745.28 61018.31 56589.87 53631.37 67215.51
## 586 587 588 589 590 591 592 593
## 52629.00 59980.84 59072.29 52788.03 52092.47 52368.79 43075.06 45874.78
## 594 595 596 597 598 599 600 601
## 40495.69 44745.87 53518.19 46842.54 52707.58 55087.82 60762.72 56916.91
## 602 603 604 605 606 607 608 609
## 61735.60 53295.58 55177.51 55954.64 58265.18 58839.81 58380.11 52551.55
## 610 611 612 613 614 615 616 617
## 59620.44 57678.55 54772.77 51475.48 44431.20 55953.27 59844.68 50770.80
## 618 619 620 621 622 623 624 625
## 61183.45 65372.67 58855.80 81106.08 66213.64 58535.13 60540.11 55888.68
## 626 627 628 629 630 631 632 633
## 46214.19 56932.52 37468.99 37329.07 46907.13 57400.48 55443.09 84201.36
## 634 635 636 637 638 639 640 641
## 74377.59 76580.57 78219.53 72940.50 65652.37 62283.26 50175.93 48542.59
## 642 643 644 645 646 647 648 649
## 47433.04 45913.76 44296.78 46976.33 51598.40 66584.41 81023.25 78143.70
## 650 651 652 653 654 655 656 657
## 79048.38 84925.78 98384.00 93137.32 63377.44 60826.68 57777.99 58763.61
## 658 659 660 661 662 663 664 665
## 55201.00 45604.08 47990.82 52312.95 51504.34 63077.75 62955.33 63245.15
## 666 667 668 669 670 671 672 673
## 51688.97 53049.86 54069.34 49403.42 43377.12 46415.06 44011.46 47501.75
## 674 675 676 677 678 679 680 681
## 45251.93 38082.35 32734.99 32732.49 35484.24 40220.99 42483.68 40486.20
## 682 683 684 685 686 687 688 689
## 40509.81 40959.02 35295.74 41631.47 41128.42 41427.98 43425.46 54143.92
## 690 691 692 693 694 695 696 697
## 62609.67 70666.94 59953.11 55956.89 52836.12 57995.35 48278.51 41971.26
## 698 699 700 701 702 703 704 705
## 35860.88 32576.71 31055.18 36567.31 34829.16 35502.48 41441.34 70129.41
## 706 707 708 709 710 711 712 713
## 76294.13 93858.74 90174.33 92772.18 107812.27 106651.76 83991.12 84338.78
## 714 715 716 717 718 719 720 721
## 75668.52 72768.36 70742.89 53455.48 48849.27 52344.81 49847.14 38951.55
## 722 723 724 725 726 727 728 729
## 44525.01 40792.63 43039.95 40913.05 31611.20 35565.82 36191.67 29820.96
## 730 731 732 733 734 735 736 737
## 47906.37 50156.19 48188.19 53847.60 46246.80 46521.62 54510.81 40949.15
## 738 739 740 741 742 743 744 745
## 37357.68 45867.16 42638.58 44142.63 46914.00 46026.31 42441.96 49437.52
## 746 747 748 749 750 751 752 753
## 44453.81 65436.26 70761.63 66867.23 58794.84 78592.79 71660.44 70578.62
## 754 755 756 757 758 759 760 761
## 55801.74 104568.33 121655.12 126248.46 107756.81 110187.39 109434.12 107461.49
## 762 763 764 765 766 767 768 769
## 60094.39 45131.25 47042.29 45665.54 43640.20 152314.44 156755.50 181983.63
## 770 771 772 773 774 775 776 777
## 185553.49 179485.47 177480.99 184369.62 81490.58 72070.93 38414.01 41850.21
## 778 779 780 781 782 783 784 785
## 42259.57 46661.41 41054.96 41375.26 41217.73 45775.60 40507.93 40205.13
## 786 787 788 789 790 791 792 793
## 45323.88 48351.46 46604.32 43951.13 46692.39 50063.13 50469.37 45007.96
## 794 795 796 797 798 799 800 801
## 41040.22 41625.73 42036.22 36663.75 36745.57 36017.53 35908.10 47521.11
## 802 803 804 805 806 807 808 809
## 50252.88 56871.56 59022.07 53581.27 60750.10 68491.20 57351.26 50418.90
## 810 811 812 813 814 815 816 817
## 44265.43 48079.01 52451.24 50620.27 38618.38 36921.33 44506.27 52864.48
## 818 819 820 821 822 823 824
## 44507.77 38886.34 32586.58 43774.24 43952.88 41355.52 35522.09
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8106
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.957703 0.7360943 3.756658
## t2* 2656.834976 165.5203372 871.021560
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.070946 4.901075 12.91641
## 2 lag_depvar 1614.993213 2698.608882 4481.95599Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Mar 17 00:51:43 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Mar 17 00:51:53 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Mar 17 00:52:02 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Mar 17 00:52:11 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Mar 17 00:52:20 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Mar 17 00:52:30 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Mar 17 00:52:39 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Mar 17 00:52:48 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Mar 17 00:52:57 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Mar 17 00:53:07 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 0.0000 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 258.8850 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 59.9440 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 0.0000 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 0.0000 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 36.9995 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 10.9950 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.0000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 366.8235 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2655, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2655 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-04-09 00:04:58 sería de: 26.676 pesos// Percentil 95% más alto proyectado: 35.051,67
Según TimeGPT: La proyección de la UF a 298 días más 2026-02-01 sería de: 40.421,82 pesos// Percentil 80% más alto proyectado: 40.735,25 pesos// Percentil 95% más alto proyectado: 41.080,7
Según prophet: La proyección de la UF a 298 días más 2026-02-01 sería de: 39.289 pesos// Percentil 95% más alto proyectado: 42.321
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26293.96 | 26328.60 |
| Lo.80 | 26425.51 | 26491.43 |
| Point.Forecast | 26675.83 | 26799.01 |
| Hi.80 | 31453.43 | 32108.70 |
| Hi.95 | 34319.77 | 34919.48 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,1,1)
##
## Coefficients:
## ar1 ma1
## 0.4188 -0.9485
## s.e. 0.1294 0.0592
##
## sigma^2 = 37557: log likelihood = -481.1
## AIC=968.2 AICc=968.55 BIC=975.03paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4055 562.8354 14.9430
## s.e. 0.1106 328.3026 10.1052
##
## sigma^2 = 35910: log likelihood = -484.98
## AIC=977.96 AICc=978.55 BIC=987.12forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 669.6808 | 643.7799 | 629.0613 |
| Lo.80 | 810.3198 | 795.5792 | 723.3464 |
| Point.Forecast | 1075.9932 | 1083.7927 | 948.1711 |
| Hi.80 | 1341.6666 | 1375.4911 | 1241.8993 |
| Hi.95 | 1482.3056 | 1529.9070 | 1432.1579 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.5 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.12
## [10] tidytext_0.4.2 DT_0.33 autoplotly_0.1.4
## [13] rvest_1.0.4 plotly_4.10.4 xts_0.14.1
## [16] forecast_8.23.0 wordcloud_2.6 RColorBrewer_1.1-3
## [19] SnowballC_0.7.1 tm_0.7-16 NLP_0.3-2
## [22] tsibble_1.1.6 lubridate_1.9.4 forcats_1.0.0
## [25] dplyr_1.1.4 purrr_1.0.4 tidyr_1.3.1
## [28] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
## [31] sjPlot_2.8.17 lattice_0.22-6 gridExtra_2.3
## [34] plotrix_3.8-4 sparklyr_1.8.6 httr_1.4.7
## [37] readxl_1.4.5 zoo_1.8-13 stringr_1.5.1
## [40] stringi_1.8.4 DataExplorer_0.8.3 data.table_1.17.0
## [43] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [46] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.1
## [4] janitor_2.2.1 lifecycle_1.0.4 httr2_1.1.1
## [7] StanHeaders_2.32.10 globals_0.16.3 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.1.0 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.9 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] askpass_1.2.1 pkgbuild_1.4.6 DBI_1.2.3
## [22] abind_1.4-8 quadprog_1.5-8 nnet_7.3-19
## [25] rappdirs_0.3.3 inline_0.3.21 tokenizers_0.3.0
## [28] listenv_0.9.1 anytime_0.3.11 performance_0.13.0
## [31] spatial_7.3-17 parallelly_1.42.0 codetools_0.2-20
## [34] xml2_1.3.8 tidyselect_1.2.1 ggeffects_2.2.1
## [37] farver_2.1.2 urca_1.3-4 its.analysis_1.6.0
## [40] matrixStats_1.5.0 stats4_4.4.0 jsonlite_1.9.1
## [43] ellipsis_0.3.2 Formula_1.2-5 systemfonts_1.2.1
## [46] tools_4.4.0 glue_1.8.0 xfun_0.51
## [49] TTR_0.24.4 ggfortify_0.4.17 loo_2.8.0
## [52] withr_3.0.2 timeSeries_4041.111 fastmap_1.2.0
## [55] boot_1.3-30 openssl_2.3.2 caTools_1.18.3
## [58] digest_0.6.37 timechange_0.3.0 R6_2.6.1
## [61] colorspace_2.1-1 networkD3_0.4 gtools_3.9.5
## [64] generics_0.1.3 htmlwidgets_1.6.4 pkgconfig_2.0.3
## [67] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [70] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [73] carData_3.0-5 tseries_0.10-58 snakecase_0.11.1
## [76] knitr_1.50 rstudioapi_0.17.1 tzdb_0.5.0
## [79] uuid_1.2-1 nlme_3.1-164 curl_6.2.1
## [82] cachem_1.1.0 sjlabelled_1.2.0 KernSmooth_2.23-22
## [85] parallel_4.4.0 fBasics_4041.97 pillar_1.10.1
## [88] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [91] car_3.1-3 dbplyr_2.5.0 evaluate_1.0.3
## [94] cli_3.6.4 compiler_4.4.0 crayon_1.5.3
## [97] future.apply_1.11.3 labeling_0.4.3 sjmisc_2.8.10
## [100] rstan_2.32.7 QuickJSR_1.6.0 viridisLite_0.4.2
## [103] assertthat_0.2.1 munsell_0.5.1 lazyeval_0.2.2
## [106] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [109] bit64_4.6.0-1 future_1.34.0 nixtlar_0.6.2
## [112] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [115] bslib_0.9.0 quantmod_0.4.26 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))